kbondelie
3d5de06b44
AI-powered web app that recommends unwatched movies from a Jellyfin library based on natural language mood input. Jellyfin auth, modular LLM backend (Claude/OpenAI/Ollama), two-tier pre-filter + AI ranking, mobile-responsive dark theme UI with poster cards and deep links. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
101 lines
3.3 KiB
Python
101 lines
3.3 KiB
Python
import json
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import logging
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import time
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from fastapi import APIRouter, HTTPException, Request
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from app.config import settings
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from app.database import get_unwatched_movies
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from app.models import MoodRequest, MoodResponse, Recommendation
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from app.routers.auth import get_current_user
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from app.services.jellyfin import get_deep_link, get_poster_url
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from app.services.llm import get_llm_provider
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from app.services.prefilter import prefilter_candidates
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logger = logging.getLogger("movie-night.mood")
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router = APIRouter()
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@router.post("/mood", response_model=MoodResponse)
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async def get_mood_recommendations(request: Request, body: MoodRequest):
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user = await get_current_user(request)
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if not body.mood.strip():
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raise HTTPException(status_code=400, detail="Please describe what you're in the mood for")
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start_time = time.time()
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# Build list of user IDs to check watch state against
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user_ids = [user["id"]] + body.additional_user_ids
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# Get unwatched movies from cache
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unwatched_raw = await get_unwatched_movies(user_ids)
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total_unwatched = len(unwatched_raw)
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if total_unwatched == 0:
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return MoodResponse(
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recommendations=[],
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meta={
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"total_unwatched": 0,
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"candidates_evaluated": 0,
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"processing_time_ms": 0,
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"mood": body.mood,
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},
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)
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# Pre-filter to narrow candidates
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candidates = prefilter_candidates(
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unwatched_raw, body.mood, max_candidates=settings.max_candidates
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)
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logger.info(f"Pre-filtered {total_unwatched} movies to {len(candidates)} candidates for mood: '{body.mood}'")
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# Get recommendations from LLM
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try:
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llm = get_llm_provider()
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raw_recs = await llm.get_recommendations(
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body.mood, candidates, max_results=settings.max_recommendations
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)
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except Exception as e:
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logger.error(f"LLM error: {e}")
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raise HTTPException(status_code=502, detail="Failed to get recommendations from AI. Please try again.")
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# Validate and enrich recommendations
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candidate_map = {m.jellyfin_id: m for m in candidates}
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recommendations = []
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for rec in raw_recs:
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jf_id = rec.get("jellyfin_id")
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if not jf_id or jf_id not in candidate_map:
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logger.warning(f"LLM returned invalid jellyfin_id: {jf_id}")
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continue
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movie = candidate_map[jf_id]
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recommendations.append(
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Recommendation(
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jellyfin_id=movie.jellyfin_id,
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title=movie.title,
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year=movie.year,
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genres=movie.genres,
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community_rating=movie.community_rating,
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runtime_minutes=movie.runtime_minutes,
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content_rating=movie.content_rating,
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poster_url=get_poster_url(movie.jellyfin_id),
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deep_link=get_deep_link(movie.jellyfin_id),
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reasoning=rec.get("reasoning", "A great match for your mood!"),
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match_score=rec.get("match_score", 0.5),
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)
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)
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elapsed_ms = int((time.time() - start_time) * 1000)
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return MoodResponse(
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recommendations=recommendations,
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meta={
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"total_unwatched": total_unwatched,
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"candidates_evaluated": len(candidates),
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"processing_time_ms": elapsed_ms,
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"mood": body.mood,
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},
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)
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